1. Field of the Invention
The present invention is related to a data processing system based on the concept of a neural network.
2. Background Information
A neural network in a data processing system is constructed in a layer state by arranging neural cells in parallel, see model 1 in FIG. 3 ("neuron", hereinafter). The neuron in each layer combines by synapses with all the neurons in adjacent layers and inputs and outputs data. Concerning neuron 1, in FIG. 3, weights WI, W2, W3, ..., Wn are multiplied by data I1, I2, I3, . . . , In inputted from outside, and data 0 is outputted corresponding to the comparison between the sum of the multiplication and threshold .THETA..
Various methods are possible for the comparison. When normalized function 1[f]is adopted, output data 0 is expressed as formula (1). EQU 0=1 [.SIGMA.Wn.multidot.In-.THETA.]
When .SIGMA.Wn .multidot.In is more than or equal to threshold when .SIGMA.Wn .multidot.In is less than threshold .THETA., the neuron is not activated, and output data 0 is "0".
Conventional neural networks have neural layers with neurons arranged in parallel with the neural layers connected in series. Neural layers are comprised of, for example, 3 layers, namely, an input layer, a middle layer and an output layer, as Perceptrons suggested by Rosenblatt. The neuron in each layer combines with all neurons in adjacent other layers by synapses.